An Ensemble Method based on Selection Using Bat Algorithm for Intrusion Detection

Shen, Yong; Zheng, Kan; Wu, Chunhua; Zhang, Mingwu; Niu, Xinxin; Yang, Yixian

doi:10.1093/comjnl/bxx101

Cited by 56 publications

(27 citation statements)

References 22 publications

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“…Shen et al 81 proposed an IDS using an ensemble method by selecting ELM as a base classifier. To optimize the proposed methodology, a BAT optimization algorithm is used during the ensemble pruning phase.…”

Section: Ai Methods For Nidsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

583

215

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The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

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Section: Ai Methods For Nidsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

583

215

View full text Add to dashboard Cite

show abstract

“…The study concluded that random tree classifier was the winner under various performance metrics. RUSBoost [120,137,146] Not mentioned [137] LogitBoost [94,103,120] GentleBoost [53,120] LPBoost [62] RealBoost [53,56] MultiBoost [56] CatBoost [107,147] ModestBoost [53] Random subspace [46,137,146] Rotation forest [55,56,70,85,110,111] Tree Maximum probability voting [68,106,135] Product probability voting [68,135] Sum probability voting [76] Minimum probability voting [68,145] Median probability voting [106,145] Bayesian [98] Al-Jarrah et al [80] proposed a semi-supervised multi-layered clustering (SMLC) model for IDSs. The performance of SMLC was compared with that of supervised ensemble ML models and a well-known semi-supervised model (i.e., tri-training).…”

Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

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Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs.

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“…The CNN is improved to extract features across layers, and feature fusion is used to obtain comprehensive features. Shen et al [ 34 ] applied an extreme learning machine (ELM) to intrusion detection, which was found to improve the detection speed and generalization ability of the model. In the research by Zhang et al [ 35 ], a smart grid intrusion detection model that combines the genetic algorithm (GA) and ELM was proposed.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Yao

Wang

Liu

et al. 2021

Sensors

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Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.

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An Ensemble Method based on Selection Using Bat Algorithm for Intrusion Detection

Cited by 56 publications

References 22 publications

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

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